phi3nedtuned-ner-json

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

phi3nedtuned-ner-json

This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the dataset: https://huggingface.co/datasets/shujatoor/ner_instruct-json.

For Inference

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

config = PeftConfig.from_pretrained("shujatoor/phi3nedtuned-ner-json")
model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3-mini-4k-instruct", 
    device_map="cuda", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
model = PeftModel.from_pretrained(model, "shujatoor/phi3nedtuned-ner-json")
model.config.to_json_file('adapter_config.json')


torch.random.manual_seed(0)
tokenizer = AutoTokenizer.from_pretrained("shujatoor/phi3nedtuned-ner-json")


text = "Tehzeeb Bakers STRN3277876134234 Block A. Police Foundation,PwD Islamabad 051-5170713-4.051-5170501 STRN#3277876134234 NTN#7261076-2 Sales Receipt 05/04/202405:56:40PM CashierM J Payment:Cash Rate Qty. Total # Descriptlon 80.512.000 190.00 1.VEGETABLESAMOSA Sub Total 161.02 Total Tax: 28.98 POS Service Fee 1.00 Total 191.00 Cash 200.00 Change Due 9.00 SR#th007-220240405175640730 Goods Once Sold Can Not Be Taken Back or Replaced All Prices Are Inclusive Sales Tax 134084240405175640553"
q_json = "extracted_data': {'store_name': '', 'address': '', 'receipt_number': '', 'drug_license_number': '', 'gst_number': '', 'vat_number': '', 'date': '', 'time': '', 'items': [], 'total_items': '', 'gst_tax': '', 'vat_tax': '', 'gross_total': '', 'discount': '', 'net_total': '', 'contact': ''}"
qs = f'{text}. {q_json}'
print('Question:',qs, '\n')
messages = [
    #{"role": "system", "content": ""},
    {"role": "user", "content": qs},

]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 512,
    "return_full_text": False,
    #"temperature": 0.0,
    "do_sample": False,
}

output = pipe(messages, **generation_args)

print('Answer:', output[0]['generated_text'], '\n')

Model description

More information needed

Intended uses & limitations

Named Entity Recognition (NER)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 0
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.2
• num_epochs: 5

Training results

Framework versions

• PEFT 0.10.1.dev0
• Transformers 4.41.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1